Certain types of machinery perform repetitive motions during operations, such as a hydraulic excavator which performs repeated motions such as digging and loading during earthmoving operations. Currently, development of systems to automate control of earthmoving and other types of machinery is underway to alleviate the need for human operators and to accomplish tasks as quickly and precisely as possible. As used in this patent specification the phrase "earthmoving machine" and various approximations thereof refer to excavators, wheel loaders, track-type tractors, compactors, motor graders, agricultural machinery, pavers, asphalt layers, and the like, which exhibit both (1) mobility over or through a work site, and (2) the capacity to alter the topography or geography of a work site with a tool or operative portion of the machine such as a bucket, shovel, blade, ripper, compacting wheel and the like.
There are systems currently developed for robotic machinery that "learn" during operations. The "learning" typically includes storing a series of steps for performing a function, such as digging and dumping, and repeating the steps as many times as instructed. The current learning functions are designed to replicate repetitive tasks to alleviate the need for an operator to perform the same task several times. Conditions at a work site may change frequently, however, making a programmed series of steps less efficient as the conditions change. For example, at an excavating site, the shape of the terrain at the dig face constantly changes, the amount and distribution of material in a truck bed changes as loads of material are added. Further, the characteristics of the material being excavated, for example, large boulders, rocks, gravel, loose sand, or sticky clay, may change as new layers of soil are exposed. A programmed series of steps that may be highly efficient at the beginning of a task may become less efficient as the work progresses.
A technical paper authored by B. Song and A. Koivo entitled "Neural Adaptive Control of Excavators", Proceedings of International Conference on Intelligent Robots and Systems, Vol. 1, pp. 162-167, discloses a control system having a feed-forward torque term to adjust the digging plan in response to changes in the texture of material being excavated. The torque term is computed by a neural network trained to compute the inverse dynamics of the excavator. Although the addition of the feed-forward torque term improved overall tracking and stability, neural networks require considerable computation time to train, but once trained, predictions are computed very quickly. An additional drawback with neural networks is that they must be retrained to incorporate information from new data, i.e. they do not easily adapt autonomously to changes in the environment.
Rule based systems for controlling operations at a dig site are also used as disclosed, for example, by D. Seward in "LUCIE--The Autonomous Robot Excavator", Industrial Robot International Quarterly, Vol. 19, No. 1, pp. 14-18. These systems typically require a large number of rules to deal with variable conditions during excavation, and the rules must be implemented prior to the start of operations. The systems do not have the ability to adjust the rules to handle unforeseen situations or to optimize motions based on past experience. It is also unclear how the parameters or thresholds in the rules are generated.
U.S. patent application Ser. No. 08/796,824, which is assigned to the same assignee as the present application, discloses an automated system and method for controlling movement of machinery using parameterized scripts. Different sets of script parameters may be chosen depending on the work mode or upon the occurrence of different events. The parameters of a motion, such as for a hydraulic excavator, are computed using inverse kinematics, joint velocity information, and various heuristics. Some of the heuristics are used to compute the parameters dealing with soil conditions. The angles between the moving components may be very different depending on characteristics of the excavated material, for example dry sand or wet mud, and a wrong angle may result in inaccurate soil placement. The equations which compute these parameters do not change unless they are reprogrammed in the system. Thus, if the excavator performs poorly because of a bad assumption or heuristic, then there is currently no way to rectify the performance without interrupting operation of the machine.
A system that is able to autonomously monitor the work progress and modify the programming during operations so that the machine performs efficiently over a wide range of digging and loading conditions is therefore desirable.
Accordingly, the present invention is directed to overcome one or more of the problems as set forth above.